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COMMUNICATION THEORY 7CCEMCTH
Creation date：2024-04-27 17:42:44
Belonging category：电气工程electric engineering
COMMUNICATION THEORY

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COMMUNICATION THEORY

7CCEMCTH
COURSEWORK

There are 3 Questions, answer all.
Detailed answers and calculations are required.

Upload clearly scanned copies of your written answers by
the deadline, as indicated on Keats.

1
Question 1 - Infinite-length design for QAM channel equalisation [40 marks]:

The discrete-time deterministic autocorrelation function of a filtered AWGN channel is given
as:
() = 0.5−1 + 1 − 0.5

The signal-to-noise ratio matched filter bound is SNRMFB = 50 and
ℰ̅
2
= 25, both in linear
scale, where ℰ̅ is the average transmit energy per dimension and
2 is the noise variance.
Successive QAM symbols with average transmit power =
ℰ

= 0dBm, where is the
symbol period, are transmitted through this channel.
The noise variance is 2 = −80dBm/Hz. The gap at symbol error probability of = 10
−6 is
Γ = 8.8dB.

a) Design MMSE-LE for this channel by finding its filter transform and compute the
corresponding unbiased signal-to-noise ratio. (15 marks)

b) Design MMSE-DFE for this channel by finding its filter transforms and compute the
corresponding unbiased signal-to-noise ratio. (15 marks)

c) Determine which one of your designs in parts (a) and (b) can provide the highest
data rate with integer number of bits per symbol at the target probability of symbol
error of = 10
−6, and compute that maximum achievable data rate on this
channel. (10 marks)























2
Question 2 – Finite-length design [20 marks]:
Consider a filtered AWGN channel with impulse response modelled as:

ℎ() = () − .5( − ).
The transmit filter (basis function) is given as () = √
1

sinc (


), where is the symbol
period. The noise variance is 2 = 0.125, the average transmit energy per dimension is
ℰ̅ = 1. An oversampling factor of = 1 is assumed. The impulse response of the anti-
aliasing filter at the receiver is given as ℎ() =
1

sinc (


), i.e., flat in the frequency
domain with a gain of one.

Hint: sinc (
+

) ∗ sinc (
+

) = sinc (
+(+)

), where ∗ means convolution.

Important attention: The objective of this question is to explore the calculations of the
formulations in lecture notes in details. You may use MATLAB for any matrix manipulations
required for completing your solution. You may also validate your detailed solutions and
answers with the MATLAB DFE programme in KEATS, after you have solved the problem and
written down your solutions in details.

a) Find the pulse response () of the channel for finite-length design and the power
gain ‖‖2, corresponding to the discrete-time channel. (6 marks)

b) Find the signal-to-noise ratio matched filter bound, SNRMFB (in dB). (2 marks)

c) Design a 3 tap FIR MMSE-LE for delay Δ = 0. (8 marks)

d) Find the −
2 for your design in part (c). (2 marks)

e) Compute the unbiased SNR for the MMSE-LE. (2 marks)

















3
Question 3 – Finite-length design and evaluations with oversampling and polyphase
channel modelling [40 marks]:
Consider a filtered AWGN channel with impulse response ℎ() =
1

1
1+(
107
3
)2
and the
frequency response (Fourier transform of ℎ()):

() =
3
10
−6(10
−7)||.

QAM transmission with symbol rate of 1 MHz and carrier frequency = 600 KHz is used on
this channel with oversampling factor of 2. The transmission system is shown in Figure 1:



Figure 1. Detailed transmission channel

As shown on Fig. 1, the power spectral density of noise is −86.5 dBm/Hz and the transmit
power is 1mW. The oversampling factor for the design of the equaliser is = 2. Square root
raised cosine pulse () with 10% of excess bandwidth (i.e., roll-off factor = 0.1) is used
as the transmit basis function. An ideal anti-aliasing filter with frequency response as
characterised on Fig. 1 is applied at the receiver input.
A MATLAB program provided on KEATS (Project 1) can be used to implement ().
According to the lecture notes the complex pulse response is modelled as the cascade of the
transmit basis function (), baseband equivalent of the channel impulse response,
ℎ(), and the impulse response of the ideal anti-aliasing filter, ℎ():
() = () ∗ ℎ() ∗ ℎ().

a) Using MATLAB programming, find the complex discrete-time pulse response samples
and write down as your answer for part (a) only the 8 samples around the peak value
(i.e., centred around time origin = 0 , with time zero included), as the truncated
pulse response for practical design.
(20 marks)

Hint: Using MATLAB, first find the continuous-time Fourier transform of () in
baseband for which you will need to find ( + ) =
3
10
−6(10
−7)|+|. You will
also need square root raised cosine pulse in frequency domain, i.e., √() in lecture
notes, whose MATLAB program can be found in KEATS (Project 1). With = 2, your
sampling period will be

2
. Then you will be able to find the discrete-time Fourier
transform (DTFT) of the oversampled discrete-time pulse response. Then using
inverse DTFT, i.e., -point IFFT in MATLAB, you will be able to find the samples of
4
the pulse response. To avoid aliasing in discrete-time domain, use large enough,
i.e., = 1024. After obtaining discrete-time samples of pulse response, truncate
it around the peak of the pulse response, i.e., around the time origin, and keep 8
samples.
Here is a minimum programming steps, suggested for part (a):

T = 1*10^(-6); % Symbol Period
fc = 6*10^(5); % Carrier Frequency
alpha = 0.1; % Excess Bandwidth
l=2; %oversampling factor
N = 1024; % Number of FFT points
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Form raise cosine pulse in frequency-domain, i.e. √(),
as the continuous-time Fourier transform of ().
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Form the continuous-time channel baseband spectrum, i.e.,
( + ) =
3
10
−6(10
−7)|+|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Form the continuous-time equivalent channel pulse
response for finite-Length in frequency-domain, i.e., ()
as the continuous-time Fourier transform of
() = () ∗ ℎ() ∗ ℎ().
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Form the discrete-time Fourier-transform (DTFT) of the
discrete-time sequence = (

2
).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Use the N-point IFFT command to find the discrete-time
samples of the pulse response .

Use the ifftshift command to centre time samples about
the time origin

Record 8 time samples of (4 samples to the left of the
time origin, sample at the origin and 3 samples to the
right of the time origin).

Continue Question 3 with the following remaining parts, which are independent from Part
(a):
• The discrete-time complex pulse response at carrier frequency = 550 KHz, the symbol
rate of 1 MHz for QAM transmission through the transmission system shown and detailed in
Fig. 1 with oversampling factor of = 2 is given as:

() = (0.022 − 0.053)−4 + (0.097 + 0.03)−3 + (−0.046 + 0.1)−2 +
(−0.23 − 0.15)−1 + 0.4 + (−0.23 + 0.15) + (−0.046 − 0.1)2
+ (0.97 − 0.03)3

5
b) After applying delay to make causal, express the obtained samples as causal 2-
tuple representation of the polyphase channel pulse response, i.e., calculate the
matrix = [0 1 ⋯ ]. (5 marks)

c) Calculate channel power gain ‖‖2 in dB. (3 marks)
Hint: Use ‖‖2 =
1
2
∑ |(

2
)|
2


d) Calculate signal-to-noise ratio matched filter bound. (2 marks)

e) Using the DFE programme in KEATS, design an FIR MMSE-LE with 10 taps and find
the corresponding achievable data rate at probability of symbol error of 10−6.
(5 marks)

f) Using the DFE programme in KEATS, design an FIR MMSE-DFE with 10 feedforward
taps and 4 feedback taps and find the corresponding achievable data rate at
probability of symbol error of 10−6.
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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